MEAT TRADE DYNAMICS: MONTHLY PATTERNS IN ROMANIA’S IMPORTS (CMA AND HOLT-WINTERS METHODS) by Aurelia Vasilica BĂLAN, Elena TOMA

“…The Holt-Winters model proved more accurate, predicting higher future imports with more appropriate values. …”
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Remote Management of Heart Failure in Patients with Implantable Devices by Luca Santini, Francesco Adamo, Karim Mahfouz, Carlo Colaiaco, Ilaria Finamora, Carmine De Lucia, Nicola Danisi, Stefania Gentile, Claudia Sorrentino, Maria Grazia Romano, Luca Sangiovanni, Alessio Nardini, Fabrizio Ammirati

“…<b>Results</b>: Precise multi-parameter algorithms, available for ICD and CRT-D patients, have been created, which also use artificial intelligence and are able to predict a new heart failure event more than 30 days in advance. …”
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T2 <italic>Candida</italic> Panel: A Game Changer in Diagnosis of Fungal Infections by Devesh N Joshi, Bhaskar Shenoy

“…In low-prevalence settings, the positive predictive value of T2MR might not be enough to justify the initiation of antifungal treatment in itself.…”
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Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics by Jinghong Pei BD, Jing Yu BD, Ping Ge BD, Liman Bao BD, Haowen Pang MS, Huaiwen Zhang MS

“…To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). …”
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FAR1 as a ferroptosis-related biomarker and potential therapeutic target in acute kidney injury: integrated bioinformatics and experimental validation by Hao Duan, Jie Yan, Xingyu Fan, Yijun Du, Xing Zhong, Tianrong Pan, Yue Wang

“…Eight diagnostic biomarkers were selected using multiple algorithms, and their predictive accuracy was validated through ROC curve analysis. …”
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Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…Our study, built on the “Danish CareForColon2015 trial (cfc2015)” is aimed at closing this gap, by focusing on the full integration of AI in CCE’s pathway, where image processing steps linked to the detection, localization and characterisation of important findings are carried out autonomously using various AI algorithms. We developed a family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings. …”
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Integration of Microarray Data and Single-Cell Sequencing Analysis to Explore Key Genes Associated with Macrophage Infiltration in Heart Failure by Rao J, Wang X, Wang Z

“…The intersection of the results from machine learning revealed that SERPINA3, GPAT3, ANPEP, and FCER1G can serve as feature genes and form a diagnostic model with a good predictive capability. Unsupervised consensus clustering algorithms reveal the immune and metabolic subtypes of macrophages. …”
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Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model by Xiaolong Teng, Jiahui Xu, Xiangbin Cui, Guitao Shi, Zhengyi Hu, Qingyu Gu, Bailang Yu, Jianping Wu, Yan Huang

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Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model by Meriem Ben Lazreg, Sabeur Jemmali, Bilal Manai, Mahmoud Hamouda

“…The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP‐EKF and 0.68% with the FP‐SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP‐EKF, and 0.82% with the FP‐SVSF, compared to the same algorithms without the proposed FP method. …”
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Improved Synthesis of Global Irradiance with One-Minute Resolution for PV System Simulations by Martin Hofmann, Stefan Riechelmann, Cristian Crisosto, Riyad Mubarak, Gunther Seckmeyer

“…A comparison based on measurements performed in Lindenberg, Germany, and Carpentras, France, shows a reduction of the frequency distribution root mean square errors of more than 60% compared to the two existing synthesizing algorithms.…”
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Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning by Chengping Chai, Omar Marcillo, Monica Maceira, Junghyun Park, Stephen Arrowsmith, James O. Thomas, Joshua Cunningham

“…The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. …”
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Uncovering Key Topics in Indonesian Political Discourse Through Twitter Analysis After the 2024 Presidential Inauguration Using Clustering methods by Syarif Hidayatullah, Ulfa Siti Nuraini

“…PCA is used for dimensionality reduction. The clustering algorithms K-means, DBSCAN, PAM, and Agglomerative Hierarchical are employed, with performance evaluated based on the Silhouette Score. …”
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Load recognition method based on convolutional neural network and attention mechanism by ZHAO Yitao, LI Zhao, LIU Xinglong, LUO Zhao, WANG Gang, SHEN Xin

“…Non-intrusive load monitoring (NILM) of residential houses is an important research content of the user demand side of smart grids, and the energy consumption analysis and power consumption management of residential loads are key steps in achieving energy conservation, emission reduction, and sustainable development. Aiming at the problems of poor recognition performance of traditional algorithms and difficulty in adapting to the current complex electricity environment, a NILM load recognition method integrating convolutional neural network (CNN)-self-attention mechanism is proposed from the optimization idea of enhancing the feature extraction performance of classification algorithms. …”
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A YOLOv8n-T and ByteTrack-Based Dual-Area Tracking and Counting Method for Cucumber Flowers by Liyang Su, Shujuan Zhang, Hongtu Zhang, Xiangsen Meng, Xiongkui He

“…Accurate counting of cucumber flowers using intelligent algorithms to monitor their sex ratio is essential for intelligent facility agriculture management. …”
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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…Under the iqr_1.5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. …”
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Hardware Implementation for Triaxial Contact-Force Estimation from Stress Tactile Sensor Arrays: An Efficient Design Approach by María-Luisa Pinto-Salamanca, Wilson-Javier Pérez-Holguín, José A. Hidalgo-López

“…This paper describes the hardware implementation of the proposed sparse algorithm and that of an algorithm previously reported in the literature, comparing the results of both hardware implementations with the software results already validated. …”
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DRFW-TQC: Reinforcement Learning for Robotic Strawberry Picking with Dynamic Regularization and Feature Weighting by Anping Zheng, Zirui Fang, Zixuan Li, Hao Dong, Ke Li

“…Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. …”
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UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness by Oleksandr Liubimov, Ihor Turkin, Valeriy Cheranovskiy, Lina Volobuieva

“…The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. …”
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HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation by Simon Schlumbohm, Julia E. Neumann, Philipp Neumann

“…An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction.…”
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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

“…The results demonstrate that the random forest algorithm represents the optimal choice when both training complexity and predictive performance are comprehensively considered. …”
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